The advent of the Internet and the Worldwide Web has produced a host of electronic commerce applications, in which users interact with content and engage in a wide variety of transactions, ranging from ordering books, CDs and other items, to participating in auctions, to downloading music, to a host of other activities. Methods and systems are widely used for tracking the behavior of online users, both individually and as groups. The output from those methods and systems are typically used to adjust the structure and content of online offerings to help attract more users, or to get current users to engage in more interaction and more transactions with the provider.
Methods and systems for analyzing online user behavior range from statistical techniques, such as collaborative filtering, to use of neural nets and similar facilities. While such methods have had some success, the promise of electronic commerce remains somewhat unfulfilled. Many online businesses have failed, and those remaining find the environment increasingly competitive. Meanwhile, many businesses find that online offerings (including their own), merely take business away from offline product offerings, or that the online offerings harm offline offerings in other ways, such as by forcing price reductions. Thus, methods and systems are needed for providing improved coordination between online and offline offerings. In addition, methods and systems are needed for allowing offline businesses to take advantage of information that can be discerned from online customer behavior.